Ethic-BERT: An Enhanced Deep Learning Model for Ethical and Non-Ethical Content Classification
This addresses the need for AI systems to handle nuanced ethical reasoning, though it appears incremental as it builds on existing BERT models with preprocessing and fine-tuning enhancements.
The paper tackled the problem of classifying ethical content by introducing Ethic-BERT, a BERT-based model, which achieved 82.32% average accuracy on a standard test and a 15.28% improvement on a hard test split.
Developing AI systems capable of nuanced ethical reasoning is critical as they increasingly influence human decisions, yet existing models often rely on superficial correlations rather than principled moral understanding. This paper introduces Ethic-BERT, a BERT-based model for ethical content classification across four domains: Commonsense, Justice, Virtue, and Deontology. Leveraging the ETHICS dataset, our approach integrates robust preprocessing to address vocabulary sparsity and contextual ambiguities, alongside advanced fine-tuning strategies like full model unfreezing, gradient accumulation, and adaptive learning rate scheduling. To evaluate robustness, we employ an adversarially filtered "Hard Test" split, isolating complex ethical dilemmas. Experimental results demonstrate Ethic-BERT's superiority over baseline models, achieving 82.32% average accuracy on the standard test, with notable improvements in Justice and Virtue. In addition, the proposed Ethic-BERT attains 15.28% average accuracy improvement in the HardTest. These findings contribute to performance improvement and reliable decision-making using bias-aware preprocessing and proposed enhanced AI model.